Predicting Interfacial Thermal Resistance by Ensemble Learning

Mingguang Chen, Junzhu Li, Bo Tian, Yas m=Mohammad Alhadeethi, Bassim Arkook, Xiaojuan Tian, Xixiang Zhang

Research output: Contribution to journalArticlepeer-review


Interfacial thermal resistance (ITR) plays a critical role in the thermal properties of a variety of material systems. Accurate and reliable ITR prediction is vital in the structure design and thermal management of nanodevices, aircraft, buildings, etc. However, because ITR is affected by dozens of factors, traditional models have difficulty predicting it. To address this high-dimensional problem, we employ machine learning and deep learning algorithms in this work. First, exploratory data analysis and data visualization were performed on the raw data to obtain a comprehensive picture of the objects. Second, XGBoost was chosen to demonstrate the significance of various descriptors in ITR prediction. Following that, the top 20 descriptors with the highest importance scores were chosen except for fdensity, fmass, and smass, to build concise models based on XGBoost, Kernel Ridge Regression, and deep neural network algorithms. Finally, ensemble learning was used to combine all three models and predict high melting points, high ITR material systems for spacecraft, automotive, building insulation, etc. The predicted ITR of the Pb/diamond high melting point material system was consistent with the experimental value reported in the literature, while the other predicted material systems provide valuable guidelines for experimentalists 27 and engineers searching for high melting point, high ITR material systems.
Original languageEnglish (US)
JournalAccepted by Computation
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-07-28
Acknowledged KAUST grant number(s): CRF-2018-3717-CRG7
Acknowledgements: The work reported was funded by the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR), under the Award Nos. CRF-2018-3717-CRG7 and CRF-2015-2996 -CRG5, and by the National Natural Science Foundation of China under Award No. 21808240.


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